Computer aided diagnosis with case-based reasoning and genetic algorithms

This article addresses breast cancer diagnosis using mammographic images. Throughout, the diagnosis is done using the mammographic microcalcifications. The aim of the work presented here is twofold. First, we introduce a back-end phase, based on machine learning techniques, in a previous computer aided diagnosis system. The two machine learning techniques incorporated are case-based reasoning and genetic algorithms. These algorithms look for improving the results obtained by human experts and the previous statistical model. On the other hand, we analyse the obtained results comparing them with the ones provided by other well-known machine learning techniques. The breast cancer dataset used in the experiments come from Girona Health Area. This database contains 216 images previously diagnosed by surgical biopsy.

[1]  Stephen M. Smith,et al.  A Non-Rigid Registration Algorithm for Dynamic Breast MR Images , 1999, Artif. Intell..

[2]  Robert M. Nishikawa,et al.  Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network , 2001, SPIE Medical Imaging.

[3]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[4]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[5]  Elisabet Golobardes,et al.  Automatic diagnosis with genetic algorithms and case-based reasoning , 1999, Artif. Intell. Eng..

[6]  Jude W. Shavlik,et al.  Machine Learning: Proceedings of the Fifteenth International Conference , 1998 .

[7]  Stephen F. Smith,et al.  Flexible Learning of Problem Solving Heuristics Through Adaptive Search , 1983, IJCAI.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.

[11]  Xavier Cufí,et al.  Shape-based feature selection for microcalcification evaluation , 1998, Medical Imaging.

[12]  D Winfield,et al.  Technology transfer in digital mammography. Report of the Joint National Cancer Institute-National Aeronautics and Space Administration workshop of May 19-20, 1993. , 1994, Investigative radiology.

[13]  Reyer Zwiggelaar,et al.  Extracting background texture in mammographic images: a co-occurrence matrices based approach , 2000 .

[14]  Christopher K. Riesbeck,et al.  Inside Case-Based Reasoning , 1989 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Heng-Da Cheng,et al.  A novel approach to microcalcification detection using fuzzy logic technique , 1998, IEEE Transactions on Medical Imaging.

[17]  P Taylor,et al.  Radiologists’ description and interpretation of microcalcifications in mammograms: A knowledge elicitation study for computerized decision support. , 2000 .

[18]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[19]  Kenneth A. De Jong,et al.  Using genetic algorithms for supervised concept learning , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

[20]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[21]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[22]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[23]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..